Creating Plots Using plotly
11.6. Creating Plots Using
In this section, we cover the basics of the
plotly Python package.
plotly is the main tool we use in this book to create plots.
plotly package has several advantages over other plotting libraries.
First, it creates interactive plots rather than static images.
When you create a plot in
plotly, you can pan and zoom to see parts of the
plot that are too small to see normally.
You can also hover over plot elements, like the symbols in a scatter plot, to
see the raw data values.
Second, it can save plots using the SVG file format, which means that
images appear sharp even when zoomed in. If you’re reading this chapter
in a PDF or paper copy of the book, we used this feature to render plot images.
Finally, it has a simple API for creating basic plots, which helps when
you’re doing exploratory analysis and want to quickly create many plots.
We’ll go over the fundamentals of
plotly in this section.
We recommend using the official
plotly documentation if you encounter
something that isn’t covered here 1.
Every plot in
plotly is wrapped in a
Figure objects keep track of what plots to draw.
For instance, a single
Figure can draw a scatter plot on the left and
a line plot on the right.
Figure objects also keep track of the plot layout, which includes the
size of the plot, title, legend, and annotations.
Let’s look at an example using the dataset of dog breeds.
dogs = pd.read_csv('data/akc.csv').dropna() dogs
|5||English Cocker Spaniel||sporting||3.33||11.66||...||medium||14.0||41.0||5-15|
43 rows × 12 columns
plotly.express module provides a concise API for making plots.
import plotly.express as px
plotly.express below to make a scatter plot of weight against height for the dog breeds.
Notice that the return value from
.scatter() is a
fig = px.scatter(dogs, x='height', y='weight', width=350, height=250) # fig is a plotly Figure object: fig.__class__
Displaying a Figure object renders it to the screen.
Figure holds one plot, but
Figure objects can hold any number of plots. Below, we create a facet of three scatter plots.
fig = px.scatter(dogs, x='height', y='weight', facet_col='size', width=650, height=250) fig.update_layout(margin=dict(t=30)) fig
These three plots are stored in
However, we don’t usually manipulate
Trace objects manually.
plotly provides functions that automatically create
facetted subplots, like the
px.scatter function we used here.
Now that we have seen how to make a simple plot, we next show how to modify plots.
11.6.2. Modifying Layout¶
We often need to change the figure’s layout.
For instance, we might want to adjust the figure margins or change the axis range.
To do this, we can use the
Let’s look at an example of a scatter plot where the title is cut off because
the plot doesn’t have large enough margins.
fig = px.scatter(dogs, x='weight', y='longevity', title='Smaller dogs live longer', width=350, height=250) fig
We can adjust the margin to give enough space for the title.
fig = px.scatter(dogs, x='weight', y='longevity', title='Smaller dogs live longer', width=350, height=250) fig.update_layout(margin=dict(t=30)) fig
.update_layout() lets us modify any property of a layout.
This includes the plot title (
title), margins (
and whether to display a legend (
plotly documentation has the full list of layout properties 2.
Figure objects also have
which are similar to
.update_layout(). These two functions let us modify
properties of the axes, like the axis limits (
range), number of ticks
nticks), and axis label (
title). Below, we adjust the range of the x-axis.
fig = px.scatter(dogs, x='weight', y='longevity', width=350, height=250) fig.update_xaxes(range=[-5, 40]) fig
plotly package comes with many plotting methods; we describe several of them in the next section.
11.6.3. Plotting Functions¶
plotly methods includes line plots, scatter plots, bar plots, box plots, and histograms.
The API is similar for each type of plot.
The dataframe is the first argument.
Then, we can specify a column of the dataframe to place on the x-axis
and a column to place on the y-axis using the
y keyword arguments.
run = pd.read_csv('data/cherryBlossomMen.csv') medians = run.groupby('year')[['time']].median().reset_index() medians
14 rows × 2 columns
# x and y are names of columns in the input dataframe px.line(medians, x='year', y='time', width=350, height=250)
lifespans = dogs.groupby('size')['longevity'].mean().reset_index() # x and y work the same for other plotting methods, like px.bar px.bar(lifespans, x='size', y='longevity', width=350, height=250)
Plotting methods in
plotly also contain arguments for making facet plots.
We can facet using color on the same plot (
color argument), or
facet into multiple subplots (
facet_row). Below are examples of each.
fig = px.scatter(dogs, x='height', y='weight', color='size', width=350, height=250) fig
fig = px.histogram(dogs, x='longevity', facet_col='size', width=600, height=200) fig.update_layout(margin=dict(t=30))
To add context to a plot, we use the ‘plotly’ annotation methods; these are described next.
To add annotations to a
Figure, we use the
Annotations have text and an arrow. The location of the arrow
is set using the
y parameters, and we can shift the
location of the text from its default position
fig = px.scatter(dogs, x='weight', y='longevity', width=350, height=250) fig.add_annotation(text='Chihuahuas live 16.5 years on average!', x=2, y=16.5, ax=30, ay=5, xshift=3, xanchor='left') fig
This section covered the basics of creating plots using the
package. We introduced the
Figure object, which is the object
uses to store plots and their layouts.
We covered the basic plot types that
plotly makes available, and
a few ways to customize plots by adjusting the layout and axes, and by
In the next section, we’ll compare
plotly to other common tools for creating
visualizations in Python.